5 Data Warehouse Design Mistakes to Avoid
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Lack of Documentation
Mistake 2: Creating haphazard metadata layer documentation
The metadata is the integrator between data processes extract, transform, load (ETL) and BI. Unfortunately, a problem arises when the metadata layer is designed solely to fit shortsighted data criteria, which results in haphazard documentation.
In order to avoid this mistake, you must add descriptions to the tables or columns at the data design stage itself. If a business user rejects a BI report, it's most likely because of a poorly designed data model that lacks an accessible description and uses inconsistent naming conventions. Prevent this by creating a metadata strategy at the data modeling stage of data warehouse design.
A data warehouse lies at the base of any business intelligence (BI) implementation project. And if you are designing your data warehouse to help you visualize your company's most relevant data (as well as streamline workflows — which can help your company cut down on redundancies significantly), then you need to map out all the areas where there is a potential for your project to fail, before you begin building.
In this article, Himanshu Sareen, CEO at Icreon Tech, has identified five design mistakes that companies should avoid.
Related Topics : APC, Resellers, Data Replication, Extract Transform and Load, Structured Data Integration
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